reinforcement learning

Reinforcement learning is a paradigm in machine learning that uses incentives- or “reinforcement”- to drive learning. The learner is conceptualized as an intelligent agent working

Ray is a general purpose distributed computing framework. Ray is used for reinforcement learning and other compute intensive tasks. It was developed at the Berkeley RISELab, a research

Ray is a general purpose distributed computing framework. At a low level, Ray provides fault-tolerant primitives that support applications running across multiple processors. At a higher

Machine learning applications are widely deployed across the software industry.  Most of these applications used supervised learning, a process in which labeled data sets are used to